Flap transplantation is a critical surgical strategy for the reconstruction of tissue defects caused by trauma, tumor resection, and congenital malformations, and its survival rate directly determines surgical efficacy and patient prognosis. Following transplantation, flaps inevitably undergo ischemia-reperfusion (I/R) injury, during which oxidative stress, inflammatory responses, and metabolic disturbances are intricately intertwined, ultimately leading to cellular injury and tissue necrosis. Recent studies have demonstrated that multiple forms of programmed cell death—including apoptosis, pyroptosis, ferroptosis, necroptosis, and PANoptosis—play central roles in flap I/R injury. The extensive crosstalk and molecular interactions among these pathways form a highly complex cell death network. Specifically, apoptosis is mediated by the imbalance of Bcl-2 family proteins and the activation of cysteine-dependent aspartate-specific protease (caspase) cascades; pyroptosis is driven by the NLRP3-caspase-1-GSDMD axis, resulting in membrane pore formation and the release of pro-inflammatory cytokines; ferroptosis is characterized by iron-dependent lipid peroxidation and dysfunction of glutathione peroxidase 4 (GPX4); necroptosis is triggered by the receptor-interacting serine/threonine-protein kinase 1 (RIPK1)-RIPK3-MLKL signaling complex, leading to membrane rupture; and PANoptosis represents an integrated form of inflammatory cell death that coordinates multiple death pathways. Importantly, these forms of programmed cell death are not independent but are interconnected through extensive signaling crosstalk. Key regulatory molecules, including caspase-8, reactive oxygen species (ROS), nuclear factor-κB (NF-κB), and nuclear factor erythroid 2-related factor 2 (Nrf2), collectively modulate the dynamic balance among these pathways. Therefore, the multidimensional interplay and spatiotemporal dynamics of programmed cell death constitute a fundamental pathological basis of flap I/R injury. This review systematically summarizes the latest advances in the mechanisms and interactions of various programmed cell death pathways in flap I/R injury, aiming to elucidate the underlying regulatory network. These insights may provide novel theoretical foundations for optimizing flap protection strategies, improving flap survival, and promoting tissue repair.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Metastasis remains the primary cause of cancer-related mortality worldwide. Circulating tumor cells (CTCs) represent critical targets for metastasis prevention and treatment. Traditional Chinese medicine may prevent lung cancer metastasis through long-term intervention in CTC activity. Tiao-Shen-Zhi-Ai Formular (TSZAF) represents a Chinese medicine compound prescription utilized clinically for lung cancer treatment. This study combined three principal active ingredients from TSZAF into a novel TSZAF monomer combination (TSZAF mc) to investigate its anti-metastatic effects and mechanisms. TSZAF mc demonstrated significant inhibition of proliferation, migration, and invasion in CTC-TJH-01 and LLC cells, while inducing cellular apoptosis in vitro. Moreover, TSZAF mc substantially inhibited LLC cell growth and metastasis in vivo. Mechanistically, TAZSF mc significantly suppressed the Wnt/β-catenin signaling pathway and CXCL5 expression in lung cancer cells and tissues. Additionally, TAZSF mc notably reduced neutrophil infiltration in metastatic lesions. These findings indicate that TSZAF mc inhibits lung cancer growth and metastasis by suppressing the Wnt/β-catenin signaling pathway and reducing CXCL5 secretion, thereby decreasing neutrophil recruitment and infiltration. TSZAF mc demonstrates potential as an effective therapeutic agent for lung cancer metastasis.
Lung Neoplasms/genetics* ; Wnt Signaling Pathway/drug effects* ; Animals ; Humans ; Drugs, Chinese Herbal/pharmacology* ; Mice ; Neoplasm Metastasis/prevention & control* ; Cell Proliferation/drug effects* ; Cell Line, Tumor ; Neutrophil Infiltration/drug effects* ; Down-Regulation/drug effects* ; Cell Movement/drug effects* ; beta Catenin/genetics* ; Apoptosis/drug effects* ; Mice, Inbred C57BL ; Male ; Neoplastic Cells, Circulating/drug effects*

Objective·To explore the causal relationship between air pollution and the risk of Alzheimer's disease(AD)by using two-sample Mendelian randomization(MR).Methods·Based on the data from the genome-wide association study(GWAS),a two-sample MR analysis was conducted to evaluate the causal relationship between air pollution and the risk of AD.Air pollution indicators,including particulate matter 2.5(PM2.5),particulate matter 2.5-10(PM2.5-10),particulate matter 10(PM10),nitrogen dioxide and nitrogen oxides,were used as exposure factors,and summarized data were aggregated from the UK Biobank database.The PM2.5 dataset included 423 796 cases,with correlation analysis conducted on 9 851 867 single nucleotide polymorphisms(SNPs);the PM2.5-10 dataset included 423 796 cases,with correlation analysis conducted on 9 851 867 SNPs;the PM10 dataset included 455 314 cases,with correlation analysis conducted on 9 851 867 SNPs;the nitrogen dioxide dataset included 456 380 cases,with correlation analysis conducted on 9 851 867 SNPs;the nitrogen oxides dataset included 456 380 cases,with correlation analysis conducted on 9 851 867 SNPs.AD was used as the outcome factor,and data were obtained from the International Genomics of Alzheimer's Project(IGAP).The AD dataset included 25 580 cases and 48 466 controls,with correlation analysis of 7 067 513 SNPs.SNPs significantly associated with AD were used as instrumental variables.The main analysis was conducted by using the inverse variance weighted(IVW)method,and four methods including weighted median,MR-Egger regression,mode-based simple estimation and mode-based weighted estimation were used for quality control.Heterogeneity testing,gene pleiotropy testing and sensitivity analysis were conducted to assess the reliability of the study results.Results·Heterogeneity testing indicated no evidence of heterogeneity among SNPs associated with air pollution indicators and AD(both IVW and MR-Egger results,P＞0.05).Gene pleiotropy testing did not detect any pleiotropic effects(MR-Egger results,P＞0.05).Sensitivity analysis confirmed the stability of the PM2.5 results.IVW analysis revealed a statistically significant association between PM2.5 and AD in European populations(P＜0.001),while no statistically significant associations were observed between PM2.5-10(P=0.664),PM10(P=0.664),nitrogen dioxide(P=0.284),nitrogen oxides(P=0.567)and AD.Conclusion·There is a significant causal relationship between PM2.5 exposure and the risk of AD,with PM2.5 exposure increasing the incidence of AD.However,no evidence has been found to suggest that PM2.5-10,PM10,nitrogen dioxide or nitrogen oxides cause an increased risk of AD.

Objective·To explore the causal relationship between air pollution and the risk of Alzheimer's disease(AD)by using two-sample Mendelian randomization(MR).Methods·Based on the data from the genome-wide association study(GWAS),a two-sample MR analysis was conducted to evaluate the causal relationship between air pollution and the risk of AD.Air pollution indicators,including particulate matter 2.5(PM2.5),particulate matter 2.5-10(PM2.5-10),particulate matter 10(PM10),nitrogen dioxide and nitrogen oxides,were used as exposure factors,and summarized data were aggregated from the UK Biobank database.The PM2.5 dataset included 423 796 cases,with correlation analysis conducted on 9 851 867 single nucleotide polymorphisms(SNPs);the PM2.5-10 dataset included 423 796 cases,with correlation analysis conducted on 9 851 867 SNPs;the PM10 dataset included 455 314 cases,with correlation analysis conducted on 9 851 867 SNPs;the nitrogen dioxide dataset included 456 380 cases,with correlation analysis conducted on 9 851 867 SNPs;the nitrogen oxides dataset included 456 380 cases,with correlation analysis conducted on 9 851 867 SNPs.AD was used as the outcome factor,and data were obtained from the International Genomics of Alzheimer's Project(IGAP).The AD dataset included 25 580 cases and 48 466 controls,with correlation analysis of 7 067 513 SNPs.SNPs significantly associated with AD were used as instrumental variables.The main analysis was conducted by using the inverse variance weighted(IVW)method,and four methods including weighted median,MR-Egger regression,mode-based simple estimation and mode-based weighted estimation were used for quality control.Heterogeneity testing,gene pleiotropy testing and sensitivity analysis were conducted to assess the reliability of the study results.Results·Heterogeneity testing indicated no evidence of heterogeneity among SNPs associated with air pollution indicators and AD(both IVW and MR-Egger results,P＞0.05).Gene pleiotropy testing did not detect any pleiotropic effects(MR-Egger results,P＞0.05).Sensitivity analysis confirmed the stability of the PM2.5 results.IVW analysis revealed a statistically significant association between PM2.5 and AD in European populations(P＜0.001),while no statistically significant associations were observed between PM2.5-10(P=0.664),PM10(P=0.664),nitrogen dioxide(P=0.284),nitrogen oxides(P=0.567)and AD.Conclusion·There is a significant causal relationship between PM2.5 exposure and the risk of AD,with PM2.5 exposure increasing the incidence of AD.However,no evidence has been found to suggest that PM2.5-10,PM10,nitrogen dioxide or nitrogen oxides cause an increased risk of AD.

Objective To explore the effect of collateral status on prognosis in elderly patients with acute ischemic stroke due to large vessel occlusion(AIS-LVO)after Solitaire stent retriever in combination with the intracranial support catheter aspiration for mechanical thrombectomy(SWIM),and construct a prediction model for prognosis.Methods A retrospective analysis was performed on 240 elderly AIS-LVO patients who underwent SWIM technique in our hospital be-tween February 2019 and February 2024.According to gender,age,occlusion sites and TOAST classifications,they were divided into a modeling group(180 cases)and a verification group(60 cases)in a ratio of 3∶1.Based on the results of modified Rankin scale(mRS)at 3 months after surgery,the patients in the modeling group were further divided into good prognosis subgroup(mRS score:0-2,97 cases)and poor prognosis subgroup(mRS score:3-6,83 cases).Multivari-ate logistic regression analysis was applied to evaluate the relationship between preoperative col-lateral circulation status and prognosis and to identify the influencing factors for prognosis.Then a prediction model for prognosis was constructed,and its performance was evaluated by ROC curve analysis.Results In the modeling group at 3 months of follow-up,the poor prognosis subgroup had significantly larger proportions of posterior circulation occlusion,cardiogenic embolism,ASITN/SIR grades 3-4 and hemorrhage transformation,higher NIHSS score at admission and longer interval from onset to vascular recanalization,while lower ASPECTS score at admission when compared with the good prognosis subgroup(P＜0.01).Multivariate logistic regression analysis showed that occlusion site,TOAST classification,NIHSS score at admission,interval from onset to vascular recanalization and hemorrhage transformation were independent risk fac-tors for poor prognosis,while ASPECTS score at admission and collateral circulation were protec-tive factors of good prognosis in the elderly AIS-LVO patients after SWIM technique(P＜0.01).Hosmer-Lemeshow test showed that the regression equation obtained goodness of fit in the mod-eling group(P=0.435).ROC curve analysis revealed that the AUC,sensitivity and specificity of then constructed prediction model for poor prognosis was 0.855[95％CI(0.797-0.913)],81.93％and 79.38％,respectively.The model was further verified in the data of the verification group(34 cases in good prognosis and 26 cases with poor prognosis),the AUC value,sensitivity and speci-ficity was 0.839[95％CI(0.732-0.947)],84.62％and 79.41％,respectively.Conclusion Our pre-diction model constructed based on screened risk factors for poor prognosis has good validity in patients with AIS-LVO after SWIM technique,which can identify the patients at high risk for poor prognosis.

Objective To explore the effect of collateral status on prognosis in elderly patients with acute ischemic stroke due to large vessel occlusion(AIS-LVO)after Solitaire stent retriever in combination with the intracranial support catheter aspiration for mechanical thrombectomy(SWIM),and construct a prediction model for prognosis.Methods A retrospective analysis was performed on 240 elderly AIS-LVO patients who underwent SWIM technique in our hospital be-tween February 2019 and February 2024.According to gender,age,occlusion sites and TOAST classifications,they were divided into a modeling group(180 cases)and a verification group(60 cases)in a ratio of 3∶1.Based on the results of modified Rankin scale(mRS)at 3 months after surgery,the patients in the modeling group were further divided into good prognosis subgroup(mRS score:0-2,97 cases)and poor prognosis subgroup(mRS score:3-6,83 cases).Multivari-ate logistic regression analysis was applied to evaluate the relationship between preoperative col-lateral circulation status and prognosis and to identify the influencing factors for prognosis.Then a prediction model for prognosis was constructed,and its performance was evaluated by ROC curve analysis.Results In the modeling group at 3 months of follow-up,the poor prognosis subgroup had significantly larger proportions of posterior circulation occlusion,cardiogenic embolism,ASITN/SIR grades 3-4 and hemorrhage transformation,higher NIHSS score at admission and longer interval from onset to vascular recanalization,while lower ASPECTS score at admission when compared with the good prognosis subgroup(P＜0.01).Multivariate logistic regression analysis showed that occlusion site,TOAST classification,NIHSS score at admission,interval from onset to vascular recanalization and hemorrhage transformation were independent risk fac-tors for poor prognosis,while ASPECTS score at admission and collateral circulation were protec-tive factors of good prognosis in the elderly AIS-LVO patients after SWIM technique(P＜0.01).Hosmer-Lemeshow test showed that the regression equation obtained goodness of fit in the mod-eling group(P=0.435).ROC curve analysis revealed that the AUC,sensitivity and specificity of then constructed prediction model for poor prognosis was 0.855[95％CI(0.797-0.913)],81.93％and 79.38％,respectively.The model was further verified in the data of the verification group(34 cases in good prognosis and 26 cases with poor prognosis),the AUC value,sensitivity and speci-ficity was 0.839[95％CI(0.732-0.947)],84.62％and 79.41％,respectively.Conclusion Our pre-diction model constructed based on screened risk factors for poor prognosis has good validity in patients with AIS-LVO after SWIM technique,which can identify the patients at high risk for poor prognosis.